CN115980732A - Two-dimensional DOA estimation method, device and medium based on MUSIC and OMP - Google Patents

Abstract

The application provides a two-dimensional DOA estimation method, device and medium based on MUSIC and OMP, after confirming single snapshot echo data that each array element received in the two-dimensional even or non-even rectangular array, the fractal dimension MUSIC algorithm will be adopted, the snapshot data that is confirmed according to one direction array element structure is directly used, carry out one-dimensional MUSIC operation to the echo data of another direction array element, obtain the middle angle set and the pitch angle set of different targets, need not to use the space smoothing algorithm, under the prerequisite that does not lose the array aperture, angle accurate estimation has been realized, afterwards, carry out the matching process to the angle that middle angle set and pitch angle set contain respectively according to the OMP algorithm, for two-dimensional spectral peak search mode, the amount of calculation is greatly reduced, the real-time demand of multiple application scenes has been satisfied.

Description

Two-dimensional DOA estimation method, device and medium based on MUSIC and OMP

Technical Field

The present application relates to the field of array signal processing, and more particularly, to a two-dimensional DOA estimation method, apparatus, and medium based on MUSIC and OMP.

Background

In recent years, direction Of Arrival (DOA) estimation techniques have been widely used in the research Of array signal parameter estimation. For example, in the application of vehicle-mounted millimeter wave radar, a two-Dimensional area array is usually adopted to simultaneously estimate the elevation angle and the azimuth angle, so as to obtain Three-Dimensional (3D) space coordinates of a target. The two-dimensional area array may adopt a rectangular array implemented based on Multiple Input Multiple Output (MIMO) technology, and may implement a higher angular resolution using fewer antenna elements and a smaller physical aperture.

In order to improve estimation accuracy and stability, as well as angular resolution, it has been proposed to apply a Multiple Signal Classification (MUSIC) algorithm to DOA estimation of a rectangular array. However, in the two-dimensional DOA estimation scheme, a plurality of virtual snapshot data need to be generated in a spatial smoothing manner to achieve accurate estimation, which not only reduces the array aperture, but also limits applicability to uniform rectangular arrays. In addition, when two-dimensional angle estimation is performed, two-dimensional spectral peak searching is required, so that the calculation amount of the algorithm is increased rapidly, and the real-time requirement of an application scene cannot be met.

Disclosure of Invention

In order to solve the technical problem, the application provides the following technical scheme:

the application provides a two-dimensional DOA estimation method based on MUSIC and OMP, which comprises the following steps:

determining single-snapshot echo data received by each array element in the rectangular array;

performing one-dimensional multi-signal classification MUSIC operation on the echo data received by the array elements in the same direction to obtain a middle angle set and a pitch angle set of different targets; wherein, the snapshot data used by the one-dimensional MUSIC operation is determined according to the structure of the array element in the other direction, and the middle angle of any target is related to the pitch angle and the azimuth angle of the target;

according to an orthogonal matching pursuit OMP algorithm, matching processing is carried out on a plurality of intermediate angles in the intermediate angle set and a plurality of pitching angles in the pitching angle set, and target intermediate angles and target pitching angles corresponding to different targets are determined;

and obtaining a target azimuth corresponding to the target according to the target intermediate angle and the target elevation angle.

Optionally, the performing, by the one-dimensional multiple signal classification MUSIC operation on the echo data received by the array element in the same direction, a middle angle set and a pitch angle set of different targets are obtained, including:

arranging the echo data received by each array element according to the spatial sequence of the rectangular array to obtain an array echo matrix;

obtaining a first data covariance matrix aiming at the array elements in the azimuth direction according to the array echo matrix and the first dimension of the array elements in the pitch direction;

obtaining a second data covariance matrix aiming at the pitching direction array element according to the array echo matrix and the second dimension of the azimuth direction array element;

obtaining a first steering vector matrix of each array element in different directions;

and performing power spectrum peak value search according to the first data covariance matrix, the second data covariance matrix and the first guide vector matrix in the corresponding direction to obtain a middle angle set and a pitching angle set corresponding to each target in different directions.

Optionally, the performing, according to the first data covariance matrix and the second data covariance matrix, and the first steering vector in the corresponding direction, a power spectrum peak search to obtain a middle angle set and a tilt angle set corresponding to each target in different directions includes:

respectively performing characteristic decomposition on the first data covariance matrix and the second data covariance matrix, and obtaining array noise characteristic vectors corresponding to different directions according to the number of the targets;

carrying out peak value search according to the array noise characteristic vector and the first guide vector array in the same direction to obtain maximum values with the same number as the targets in the direction;

and forming a middle angle set and a pitching angle set of different targets by utilizing the angle corresponding to the maximum value in the same direction.

Optionally, the matching, according to the orthogonal matching pursuit OMP algorithm, the plurality of intermediate angles in the intermediate angle set and the plurality of pitch angles in the pitch angle set, and determining the target intermediate angles and the target pitch angles corresponding to the different targets respectively includes:

vectorizing the two-dimensional echo data received by the rectangular array to obtain corresponding one-dimensional echo data;

obtaining a second steering vector matrix corresponding to the one-dimensional echo data according to the first steering vector matrix of each array element in different directions;

performing sparse vector reconstruction by utilizing an Orthogonal Matching Pursuit (OMP) algorithm according to the second steering vector matrix, the intermediate angle set and the pitching angle set;

and matching each two-dimensional estimation angle in the two-dimensional estimation angle set according to the position of a nonzero element in the reconstructed sparse vector to obtain a target middle angle and a target elevation angle corresponding to different targets.

Optionally, the performing sparse vector reconstruction by using an Orthogonal Matching Pursuit (OMP) algorithm according to the second steering vector matrix, the intermediate angle set, and the pitch angle set includes:

combining a plurality of intermediate angles in the intermediate angle set with a plurality of elevation angles in the elevation angle set to obtain two-dimensional estimation angle sets of different targets;

according to the second steering vector matrix, a steering matrix corresponding to the two-dimensional estimation angle set is obtained;

constructing a compressed sensing model according to the guide array and the sparse vector;

and reconstructing sparse vectors in the compressed sensing model by utilizing an Orthogonal Matching Pursuit (OMP) algorithm.

Optionally, the method further includes:

and determining the signal amplitude corresponding to the target according to the numerical value of the non-zero element in the reconstructed sparse vector.

Optionally, the determining the single-snapshot echo data received by each array element in the rectangular array includes:

obtaining relative coordinates of each array element in the rectangular array in different directions and signal wavelengths of each target incident by the rectangular array;

modeling is carried out at least according to the relative coordinates and the signal wavelength to obtain an echo signal model;

and obtaining single-snapshot echo data received by each array element in the rectangular array by using the echo signal model.

The present application further proposes a two-dimensional DOA estimation apparatus based on MUSIC and OMP, the apparatus comprising:

the echo data determining module is used for determining the single-snapshot echo data received by each array element in the rectangular array;

the one-dimensional MUSIC operation module is used for carrying out one-dimensional multi-signal classification MUSIC operation on the echo data received by the array elements in the same direction to obtain a middle angle set and a pitching angle set of different targets; wherein, the snapshot data used by the one-dimensional MUSIC operation is determined according to the structure of the array element in the other direction, and the middle angle of any target is related to the pitch angle and the azimuth angle of the target;

the matching processing module is used for matching a plurality of intermediate angles in the intermediate angle set and a plurality of pitching angles in the pitching angle set according to an orthogonal matching pursuit OMP algorithm, and determining target intermediate angles and target pitching angles corresponding to different targets;

and the target azimuth angle obtaining module is used for obtaining a target azimuth angle corresponding to the target according to the target intermediate angle and the target pitch-dip angle.

Optionally, the one-dimensional MUSIC operation module includes:

the array echo matrix obtaining unit is used for arranging the echo data received by each array element according to the spatial sequence of the rectangular array to obtain array echo matrixes with different structures;

the first data covariance matrix obtaining unit is used for obtaining a first data covariance matrix aiming at the azimuth direction array element according to the array echo matrix corresponding to the azimuth direction and the first dimension of the elevation direction array element;

a second data covariance matrix obtaining unit, configured to obtain a second data covariance matrix for the elevation direction array element according to the array echo matrix corresponding to the elevation direction and a second dimension of the azimuth direction array element;

the first steering vector matrix obtaining unit is used for obtaining a first steering vector matrix of each array element in different directions;

and the peak searching unit is used for performing power spectrum peak searching according to the first data covariance matrix, the second data covariance matrix and the first guide vector matrix in the corresponding direction to obtain a middle angle set and a pitching angle set corresponding to each target in different directions.

The present application further proposes a computer-readable storage medium, on which a computer program is stored, where the computer program is loaded and executed by a processor, so as to implement the two-dimensional DOA estimation method based on MUSIC and OMP as described above.

Therefore, after single-snapshot echo data received by each array element in a two-dimensional uniform or sub-uniform rectangular array is determined, a sub-dimension MUSIC algorithm is adopted, snapshot data determined according to one-direction array element result is directly used, one-dimensional MUSIC operation is carried out on the echo data of the array in the other direction, a middle angle set and a pitch-and-tilt angle set of different targets are obtained, a space smoothing algorithm is not needed, accurate angle estimation is achieved on the premise that the aperture of the array is not lost, then angles contained in the middle angle set and the pitch-and-tilt angle set are matched according to the OMP algorithm, and the calculated amount is greatly reduced and the real-time requirements of various application scenes are met.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.

Fig. 1 is a schematic flowchart of an alternative example of a two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application;

FIG. 2 is a schematic structural diagram of a two-dimensional rectangular array suitable for the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application;

fig. 3 is a schematic flowchart of another alternative example of the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application;

fig. 4 is a schematic diagram of simulation parameters for performing simulation verification on the two-dimensional DOA estimation method based on MUSIC and OMP according to the present application;

fig. 5 is a schematic diagram of respective RMSE curves of a two-dimensional DOA estimation method based on MUSIC and OMP and a two-dimensional DOA estimation method based on two-dimensional smooth MUSIC proposed in the present application;

fig. 6 is a schematic structural diagram of an alternative example of the two-dimensional DOA estimation apparatus based on MUSIC and OMP according to the present application;

fig. 7 is a schematic diagram of a hardware structure of an alternative example of a computer device suitable for the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application.

Detailed Description

For the technical problems described in the background art, the accurate estimation of the Multiple Signal Classification (MUSIC) algorithm depends on the accurate estimation of an array covariance matrix, the accurate estimation of the array covariance matrix requires Multiple snapshot data, and the Multiple snapshot data are difficult to obtain in a highly dynamic application environment, and then the Multiple virtual snapshot data are generated in a spatial smoothing manner, so that the two-dimensional smooth MUSIC algorithm reduces the array aperture, is only suitable for an average rectangular array, and has the problems of too large calculation amount, difficulty in meeting practical application and the like.

In order to solve the above technical problem, the present application is expected to increase the operation speed Of two-dimensional Direction Of Arrival (DOA) estimation based on MUSIC, so that the present application can also be applied to real-time two-dimensional DOA estimation Of a non-uniform rectangular array. Specifically, aiming at the problem of large calculated amount, the method provides that one-dimensional MUSIC operation is performed in two dimensions respectively, and then Matching operation is performed on the estimated intermediate angle (intermediate angle variable consisting of azimuth angle and pitch angle) and pitch angle, for example, based on Orthogonal Matching Pursuit (OMP) algorithm, accurate angle Matching is realized, so that two-dimensional spectrum peak search is avoided, and the calculated amount is greatly reduced.

Aiming at the problem of obtaining a plurality of pieces of snapshot data, the method can be used for performing one-dimensional MUSIC angle estimation on one dimension of a rectangular array by adopting another piece of data as the multiple pieces of snapshot data to perform operation without using a space smoothing algorithm, so that the multiple pieces of snapshot data can be obtained on the premise of not losing the aperture of the array, and accurate MUSIC angle estimation is realized.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.

Referring to fig. 1, a schematic flowchart of an optional example of a two-dimensional DOA estimation method based on MUSIC and OMP provided in the present application is shown, where the method may be executed by a computer device, where the computer device includes a terminal device with certain data computing capability, such as a smart phone, a notebook computer, and a vehicle-mounted terminal, and may also include a server, such as a physical server or a cloud server, and the present application does not limit a product type of the computer device. As shown in fig. 1, the method may include:

s11, determining single-snapshot echo data received by each array element in the rectangular array;

in practical application, the two-dimensional area array usually comprises a matrix array, an L-shaped array, a circular array and the like, and compared with the L-shaped array and the circular array, array elements of the rectangular array are arranged more densely, and accurate estimation of two-dimensional angles can still be achieved under the condition of single snapshot or few snapshots, so that the rectangular array is selected to be used for two-dimensional DOA estimation.

The rectangular array can be a two-dimensional uniform/non-uniform array, the two-dimensional uniform area array is taken as an example for explanation, the two-dimensional DOA estimation implementation process of the non-uniform rectangular array is similar, and detailed description is not given in the application. Referring to the schematic diagram of the array structure of the two-dimensional uniform area array shown in fig. 2, M × N array elements are uniformly distributed on the xoz plane, and it is assumed that the relative coordinates of the M array elements in the X-axis direction (which may be referred to as the azimuth direction) are X = [0,x, respectively ₁ ,x ₂ ,……,x _M-1 ] _M×1 The relative coordinates of the N array elements in the Z-axis direction are Z = [0,z = [ respectively ₁ ,zx ₂ ,……,z _N-1 ] _N×1 。

In practical applications, if there are K far-field signals (referred to as targets in this application) incident on the rectangular array, the azimuth angle corresponding to the kth target (i.e. incident azimuth angle) is θ _k And a pitch angle of phi _k . The influence of noise is ignored, echo modeling can be performed by using at least the parameters, and the realization method of the echo modeling is not limited by the application. It should be noted that the numerical values of the above-listed parameters are not limited in the present application and may be determined as appropriate.

Intermediate angle alpha of kth target directly obtained due to two-dimensional DOA estimation technique _k And a pitch angle phi _k Wherein the intermediate angle alpha _k Is the azimuth angle theta to the target _k And a pitch angle phi _k Intermediate angle variable defined by combination, the three have sin alpha _k ＝sinθ _k cosφ _k Therefore, after obtaining the two-dimensional DOA estimation result, the present application can determine the azimuth angle of the corresponding target according to the trigonometric function relationship, and how to obtain the intermediate angle α of each target will be described below _k And a pitch angle

The implementation of (1) is described.

Step S12, carrying out one-dimensional MUSIC operation on the echo data received by the array elements in the same direction to obtain a middle angle set and a pitching angle set of different targets;

in the embodiment of the present application, to a problem that a calculation amount of a two-dimensional MUSIC (Multiple Signal Classification) algorithm is large, a dimensionality-divided MUSIC algorithm is proposed, that is, a one-dimensional MUSIC operation is performed in two dimensions (such as an x-axis direction and a z-axis direction shown in fig. 2) respectively, and respective intermediate angles and pitch-and-tilt angles of K targets are estimated to obtain an intermediate angle set a and a pitch-and-tilt angle set Φ of different targets. Wherein the intermediate angle set a = { α = ₁ ,α ₂ ,……,α _K ,} _K×1 Set of pitch angles phi = { phi = [ ] ₁ ,φ ₂ ,……,φ _K } _K×1 The value for K may be contingent and for any target intermediate angle α associated with the pitch angle Φ and the azimuth angle θ of that target may be in accordance with, but is not limited to, the trigonometric transformation relationship described above.

It should be noted that, in the execution process of the above one-dimensional MUSIC operation in different dimensions, the characteristic that the antenna array is a rectangular array is fully utilized, and snapshot data is determined in another dimension, that is, the snapshot data used in the above one-dimensional MUSIC operation is determined according to an array element structure in another direction.

For example, in the one-dimensional MUSIC operation in the azimuth direction, the array element number N in the elevation direction can be used as snapshot data; in the one-dimensional MUSIC operation of pitching the direction, can use the array element column number M of azimuth direction as snapshot data, it can be seen that the fractal dimension MUSIC algorithm that this application provided need not use the smooth algorithm of space to acquire virtual snapshot data to overcome because of using the smooth algorithm of space and led to the fact a series of problems, realized the accurate estimation of angle under the different grade type rectangle array, improved the practicality. The operation principle of MUSIC algorithm is not described in detail in this application.

Step S13, matching a plurality of intermediate angles in the intermediate angle set and a plurality of elevation and depression angles in the elevation and depression angle set according to an orthogonal matching pursuit OMP algorithm, and determining target intermediate angles and target elevation and depression angles corresponding to different targets;

because the angle estimation is performed by using the MUSIC algorithm, a one-to-one correspondence relationship is not established between a plurality of intermediate angles in the obtained intermediate angle set and a plurality of pitch angles in the pitch angle set, the angles need to be matched by using an angle matching algorithm, and a target intermediate angle and a target pitch angle corresponding to the same target are determined. To this end, in order to avoid searching for a two-dimensional spectral peak and reduce the amount of calculation, the method can realize angle accurate Matching by using an Orthogonal Matching Pursuit (OMP) algorithm, and can also realize accurate estimation of signal reflection amplitude at the same time, and the method does not need to be described in detail in the application about the operation principle of the OMP algorithm.

And S14, acquiring a target azimuth angle of the target according to the target intermediate angle and the target pitch-and-pitch angle of the same target.

In combination with the above analysis, the present application may be based on sin α _k ＝sinθ _k cosφ _k The target intermediate angle alpha of the same target _k And target pitch angle phi _k Substituting the formula to obtain the target azimuth angle alpha of the target _k But is not limited to this way of calculation.

In summary, in the embodiment of the present application, after determining single snapshot echo data received by each array element in a two-dimensional uniform or sub-uniform rectangular array, a sub-dimension MUSIC algorithm is adopted, snapshot data determined according to a result of one direction array element is directly used, and a one-dimensional MUSIC operation is performed on echo data of an array in another direction to obtain a middle angle set and a pitch angle set of different targets, without using a spatial smoothing algorithm, and under the premise of not losing an array aperture, accurate angle estimation is achieved, and then, matching processing is performed on respective included angles of the middle angle set and the pitch angle set according to an OMP algorithm.

Referring to fig. 3, which is a schematic flowchart of another optional example of the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application, this embodiment may describe an optional detailed implementation procedure of the two-dimensional DOA estimation method based on MUSIC and OMP proposed above, and as shown in fig. 3, the method may include:

step S31, determining single-snapshot echo data received by each array element in the rectangular array;

still by taking the echo modeling of the two-dimensional uniform area array (rectangular array) shown in fig. 2 described above as an example, and combining the above description of the structure of the two-dimensional rectangular array, the relative coordinates of each array element in different directions (such as the azimuth direction and the elevation and depression directions, i.e. the X-axis direction and the z-axis direction) in the uniform area array are obtained, such as X = [0,x ] ₁ ,x ₂ ,……,x _M-1 ] _M×1 ，Z＝[0,z ₁ ,zx ₂ ,……,z _N-1 ] _N×1 And after the signal wavelength lambda of K targets, echo modeling can be carried out by neglecting the influence of noise, and echo data S received by the mth array element in the x-axis direction and the nth array element in the z-axis direction are obtained _mn Can be expressed as:

in the above formula (1), M is 1. Ltoreq. M.ltoreq.N is 1. Ltoreq. N.ltoreq.N, and M and N are positive integers. exp () represents an exponential function with a natural constant e as the base. Due to the intermediate angle alpha of the same target _k Azimuth angle theta _k And a pitch angle phi _k Has sin alpha between _k ＝sinθ _k cosφ _k Substituting this into equation (1) above yields the relationship:

in combination with the idea of two-dimensional DOA estimation technology, the application canObtaining echo data received by each array element in the rectangular array by using an echo signal model shown in formula (2), and executing subsequent steps to determine a middle angle alpha in the echo data _k And a pitch angle phi _k Then according to sin α _k ＝sinθ _k cosφ _k Obtaining the azimuth angle theta of the corresponding target _k 。

Step S32, arranging echo data received by each array element according to the spatial sequence of the rectangular array to obtain an array echo matrix;

in practical application, the angle estimation of the MUSIC algorithm needs to rely on accurate estimation of a covariance matrix of a rectangular matrix, and in the process of acquiring data covariance matrices of the rectangular array in different directions, echo data received by each array element of a single-snapshot rectangular matrix can be arranged into an M × N array echo matrix S according to the array spatial sequence of the rectangular matrix, that is:

it can be seen that each element S in the array echo matrix S _mn The representation of the echo data received by the mth array element in the x-axis direction and the nth array element in the z-axis direction may be determined by combining the above equation (2).

Step S33, obtaining a first data covariance matrix aiming at the array element in the azimuth direction according to the array echo matrix and the first dimension of the array element in the pitch direction;

step S34, obtaining a second data covariance matrix aiming at the array elements in the pitching direction according to the array echo matrix and the second dimension of the array elements in the azimuth direction;

after the array echo matrix S is determined, during the one-dimensional MUSIC operation in the x-axis direction, a first data covariance matrix R of the array elements in the azimuth direction can be calculated according to the following formula (4) _x Similarly, a one-dimensional MUSIC operation is performed in the z-axis direction, and a second data covariance matrix R of the array elements in the pitch direction is calculated according to the following formula (5) _z ：

In the above formula, S ^H A conjugate transpose matrix, S, that can represent an array echo matrix, S ^T A transposed matrix of the array echo matrix S may be represented.

Step S35, obtaining a first guide vector matrix of each array element in different directions;

in order to perform spectral peak search on echo data received by arrays with different dimensions, a steering vector of each array element of the array in the x-axis direction can be obtained first, and a first steering vector matrix v in the azimuth direction is obtained _x (α)：

Similarly, the first steering vector matrix v of the z-axis direction array _z (φ)：

Step S36, performing characteristic decomposition on the first data covariance matrix and the second data covariance matrix respectively, and obtaining array noise characteristic vectors corresponding to different directions according to the number of targets;

in combination with the above description of the technical solution of the present application, a power spectrum peak search may be performed according to the first data covariance matrix, the second data covariance matrix, and the first steering vector matrix in the corresponding direction, so as to obtain a middle angle set and a pitch angle set corresponding to each target in different directions.

Specifically, the eigenvectors corresponding to the data covariance matrix are composed of the signal eigenvector and the noise eigenvector, and the above can be referred to in the applicationPerforming Eigen Decomposition (EVD) on the first data covariance matrix and the second data covariance matrix, and performing Eigen Decomposition on the first data covariance matrix corresponding to the azimuth direction to obtain a corresponding first eigenvector R _x ＝U _x ∑ _x U _x ^H Then, the first eigenvectors may be sorted according to the magnitudes of the eigenvalues, the first eigenvector corresponding to the largest K eigenvalues may be used to form a signal subspace, and the remaining first eigenvectors may be used to form a noise subspace, and thus, the first eigenvector may be transformed into R _x ＝U _xs ∑ _xs U _xs ^H +U _xn ∑ _xn U _xn ^H Wherein, U _xs The array signal subspace which can represent the horizontal direction, i.e. the array signal feature vector, U _xn The array noise subspace corresponding to the horizontal direction, i.e. the array noise feature vector, can be represented. In this way, the noise eigenvector corresponding to the pitch direction can be obtained by performing eigen decomposition on the second data covariance matrix.

Step S37, carrying out peak value search according to the array noise characteristic vector and the first guide vector array in the same direction to obtain maximum values with the same number as the targets in the direction;

step S38, forming a middle angle set and a pitching angle set of different targets by utilizing angles corresponding to the maximum values in the same direction;

when performing spectral peak search in different directions, the power spectrum in the x-axis direction can be expressed as:

the power spectrum in the Z-axis direction can be expressed as:

for the above P respectively _x (alpha) and P _z (phi) performing a peak valueSearching to obtain K maximum values of the spectrum peak, the intermediate angle set a corresponding to K maximum values searched in the x-axis direction may be represented as a = { α = [ [ α ] ] ₁ ,α ₂ ,……,α _K ,} _K×1 The set of pitch angles Φ corresponding to the K maximum values searched in the z-axis direction can be represented as Φ = { Φ = [ ] ₁ ,φ ₂ ,……,φ _K } _K×1 The implementation method of the one-dimensional spectral peak search described above is not described in detail in the present application, and the calculation amount is greatly reduced compared to the two-dimensional spectral peak search method.

Step S39, vectorizing the two-dimensional echo data received by the rectangular array to obtain corresponding one-dimensional echo data;

according to the method, the OMP algorithm can be used for reference, the obtained middle angle and the pitch angle are matched, the middle angle and the pitch angle corresponding to the same target are determined, and therefore two-dimensional echo data received by the rectangular array can be arranged into a one-dimensional echo signal S before the OMP algorithm is executed ₁ The one-dimensional echo data S can be obtained by vectorizing the array echo matrix S ₁ ：

S ₁ ＝vec(S)

＝[s ₁₁ ,s ₁₂ …s _1N ,s ₂₁ ,s ₂₂ ,…s _2N ,…,s _M1 ,s _M2 ,…s _MN ] _MN×1 (10)

In the above formula, vec () may represent a vectorization function, and the present application does not describe the vectorization implementation process of S in detail.

Step S310, according to the first steering vector matrix of each array element in different directions, obtaining a second steering vector matrix corresponding to one-dimensional echo data;

obtaining a first steering vector matrix v of array elements in different directions _z (phi) and v _x After (alpha), one-dimensional echo data S can be obtained therefrom ₁ Corresponding second steering vector matrix v _a11 (α, φ), which may be expressed as:

v _all (α,φ)＝[kron(v _z (φ),v _x (α))] _MN×1 (11)

in the above formula (11), kron () may represent Kronecker product operation, and the operation process thereof is not described in detail in this application.

Step S311, according to the second guiding vector matrix, the middle angle set and the pitching angle set, sparse vector reconstruction is carried out by utilizing an orthogonal matching pursuit OMP algorithm;

step S312, according to the position of a nonzero element in the reconstructed sparse vector, matching each two-dimensional estimation angle in the two-dimensional estimation angle set to obtain a target middle angle and a target elevation angle corresponding to different targets;

in the process of constructing the compressed sensing model, for K targets, K estimated values of the intermediate angle alpha are obtained according to the method, namely A = { alpha = (alpha) } ₁ ,α ₂ ,……,α _K ,} _K×1 And K estimated values of pitch angle phi, i.e. phi = { phi = + ₁ ,φ ₂ ,……,φ _K } _K×1 Then, K of the two-dimensional angle (alpha, phi) where the target is likely to be located can be obtained ² The utility model provides a combination, is about to combine a plurality of intermediate angles in the intermediate angle set with a plurality of angles of pitching in the angle set of pitching, obtains the two-dimentional estimation angle set χ of different targets:

χ＝{(α ₁ ,φ ₁ ),(α ₂ ,φ ₁ ),…,(α _K ,φ ₁ ),(α ₁ ,φ ₂ ),(α ₂ ,φ ₂ ),…(α _K ,φ ₂ ),…,(α ₁ ,φ _K ),(α ₂ ,φ _K ),…,(α _K ,φ _K )} (12)

may be according to the second steering vector array v described above _a11 (α, Φ), obtaining a steering matrix V corresponding to the two-dimensional estimation angle set χ:

it can be seen that the two-dimensional estimation angle set χ can be used as a complete redundant dictionary for OMP operation, and an observation signal is assumed to be denoted as S ₁ The sparse vector is denoted as x, the sparsity of x can be K, and the size can beK ² X 1, and therefore, estimating the angle set χ and the second steering vector array v according to the two dimensions _a11 (alpha, phi) respectively corresponding formulas, and constructing a compressed sensing model: s ₁ The method is characterized in that = Vx, namely, an echo signal model of a two-dimensional rectangular array is constructed by a compressed sensing method, so that the angle of a target is obtained by solving the numerical value of a variable in compressed sensing.

Then, reconstructing a sparse vector x in the compressed sensing model by using an OMP algorithm, and indexing in a two-dimensional estimation angle set χ according to the position of a non-zero element in the sparse vector x, namely obtaining a target intermediate angle and a target elevation angle of different targets corresponding to the guide vector at the position, and completing matching of the intermediate angle and the elevation angle of the same target.

Step 313, obtaining a target azimuth angle of the target according to the target intermediate angle and the target pitch angle of the same target.

Combining the above modes, the target intermediate angle alpha of the same target is obtained _k And target pitch angle phi _k Then, the trigonometric relationship among the azimuth angle, the elevation angle and the intermediate angle of the same incident signal can be used, as described above for sin α _k ＝sinθ _k cosφ _k Calculating the target intermediate angle and the target pitch angle to obtain a target azimuth angle theta _k 。

For the two-dimensional DOA estimation method based on MUSIC and OMP described in the above embodiment, the effectiveness of the present application will be verified by comparing the simulation with the two-dimensional DOA estimation method based on the two-dimensional smooth MUSIC algorithm under the simulation parameters as shown in fig. 4. Because the two-dimensional DOA estimation method based on the two-dimensional smooth MUSIC algorithm can only be applied to the uniform rectangular array, the method can perform simulation comparison centering on the uniform rectangular array, and the relative coordinate of each array element in the X-axis direction of the uniform rectangular array can be expressed as X = [0,d ] _x ,2d _x ,……,(M-1)d _x ]Relative seating of array elements in the z-axis directionThe index may represent Z = [0,d _z ,2d _z ,……,(N-1)d _z ]Wherein d is _x Can represent the fixed spacing between adjacent array elements in the x-axis direction, d _z A fixed spacing between adjacent array elements in the z-axis direction can be represented. Exemplarily, assume M = N =20,d _x ＝d _z =0.5 λ, under 5000 monte carlo simulations, root Mean Square Error (RMSE) of the two-dimensional DOA estimation methods under different Signal-to-Noise ratios (SNR) can be compared, thereby achieving the effectiveness evaluation of the two-dimensional DOA estimation method based on MUSIC and OMP proposed in this application, where the RMSE evaluation formula is as follows:

in the above formula (14), N _mc The number of Monte Carlo simulations can be expressed, N in the simulation _mc For example, K may represent the number of targets, and K =3 is described as an example in the simulation, and the target angle may be randomly generated in each monte carlo simulation.

Thus, for a given SNR, the RMSE curves for the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application and the conventional two-dimensional DOA estimation method based on the two-dimensional smoothing MUSIC algorithm are shown in fig. 5. Under 5000 Monte Carlo simulations, the average time consumed by the two-dimensional DOA estimation methods was 0.0041s and 0.7320s, respectively. Therefore, the two-dimensional DOA estimation method based on MUSIC and OMP greatly reduces the running time of the two-dimensional OMP algorithm and improves the operation efficiency.

Referring to fig. 6, a schematic structural diagram of an alternative example of the two-dimensional DOA estimation apparatus based on MUSIC and OMP proposed in the present application may include:

the echo data determining module 61 is configured to determine echo data of a single snapshot received by each array element in the rectangular array;

a one-dimensional MUSIC operation module 62, configured to perform one-dimensional multiple signal classification MUSIC operation on the echo data received by the array elements in the same direction, so as to obtain a middle angle set and a pitch angle set of different targets;

wherein, the snapshot data used by the one-dimensional MUSIC operation is determined according to the structure of the array element in the other direction, and the middle angle of any target is related to the pitch angle and the azimuth angle of the target;

a matching processing module 63, configured to perform matching processing on the multiple intermediate angles in the intermediate angle set and the multiple pitch angles in the pitch angle set according to an Orthogonal Matching Pursuit (OMP) algorithm, and determine target intermediate angles and target pitch angles corresponding to the different targets;

and a target azimuth obtaining module 64, configured to obtain a target azimuth corresponding to the target according to the target intermediate angle and the target pitch angle.

Optionally, the echo data determining module 61 may include:

the device comprises a parameter obtaining unit, a parameter calculating unit and a parameter calculating unit, wherein the parameter obtaining unit is used for obtaining relative coordinates of each array element in a rectangular array in different directions and signal wavelengths of each target incident by the rectangular array;

the echo modeling unit is used for modeling at least according to the relative coordinates and the signal wavelength to obtain an echo signal model;

and the echo data acquisition unit is used for acquiring single-snapshot echo data received by each array element in the rectangular array by using the echo signal model.

Optionally, the one-dimensional MUSIC operation module 62 may include:

the array echo matrix obtaining unit is used for arranging the echo data received by each array element according to the spatial sequence of the rectangular array to obtain an array echo matrix;

the first data covariance matrix obtaining unit is used for obtaining a first data covariance matrix aiming at the azimuth direction array element according to the array echo matrix and the first dimension of the elevation direction array element;

the second data covariance matrix obtaining unit is used for obtaining a second data covariance matrix aiming at the pitching direction array element according to the array echo matrix and the second dimension of the azimuth direction array element;

the first steering vector matrix obtaining unit is used for obtaining a first steering vector matrix of each array element in different directions;

and the spectrum peak searching unit is used for performing power spectrum peak searching according to the first data covariance matrix, the second data covariance matrix and the first guide vector matrix in the corresponding direction to obtain a middle angle set and a pitching angle set corresponding to each target in different directions.

Wherein, the spectral peak searching unit may include:

an array noise eigenvector obtaining unit, configured to perform eigen decomposition on the first data covariance matrix and the second data covariance matrix, respectively, and obtain array noise eigenvectors corresponding to different directions according to the number of targets;

the maximum value obtaining unit is used for carrying out peak value searching according to the array noise characteristic vector and the first guide vector array in the same direction to obtain the maximum values with the same number as the targets in the direction;

and the angle set forming unit is used for forming a middle angle set and a pitching angle set of different targets by using the angle corresponding to the maximum value in the same direction.

Optionally, the matching processing module 63 may include:

the vectorization processing unit is used for vectorizing the two-dimensional echo data received by the rectangular array to obtain corresponding one-dimensional echo data;

a second steering vector matrix obtaining unit, configured to obtain a second steering vector matrix corresponding to the one-dimensional echo data according to the first steering vector matrix of each array element in different directions;

the sparse vector reconstruction unit is used for reconstructing sparse vectors by utilizing an Orthogonal Matching Pursuit (OMP) algorithm according to the second steering vector matrix, the middle angle set and the pitching angle set;

and the matching unit is used for matching each two-dimensional estimation angle in the two-dimensional estimation angle set according to the position of a nonzero element in the reconstructed sparse vector to obtain a target middle angle and a target elevation angle corresponding to different targets.

Optionally, the sparse vector reconstructing unit may include:

a two-dimensional estimated angle set obtaining unit, configured to combine a plurality of intermediate angles in the intermediate angle set with a plurality of elevation angles in the elevation angle set to obtain two-dimensional estimated angle sets of different targets;

a steering matrix obtaining unit, configured to obtain a steering matrix corresponding to the two-dimensional estimation angle set according to the second steering vector matrix;

the compressed sensing model building unit is used for building a compressed sensing model according to the guide array and the sparse vector;

and the reconstruction unit is used for reconstructing the sparse vector in the compressed sensing model by utilizing an Orthogonal Matching Pursuit (OMP) algorithm.

In still other embodiments, the apparatus may further include:

and the signal amplitude determining module is used for determining the signal amplitude corresponding to the target according to the numerical value of the nonzero element in the reconstructed sparse vector.

It should be noted that, for various modules, units, and the like in the foregoing apparatus embodiments, all of which may be stored in a memory as program modules, and the processor executes the program modules stored in the memory to implement corresponding functions, and for functions implemented by the program modules and their combinations and achieved technical effects, reference may be made to the description of corresponding parts in the foregoing method embodiments, and this embodiment is not described again.

The present application further provides a computer-readable storage medium on which a computer program may be stored, the computer program being invoked and loaded by a processor to perform the steps of the two-dimensional DOA estimation based on MUSIC and OMP described in the above embodiments.

Referring to fig. 7, a schematic diagram of a hardware structure of an alternative example of a computer device suitable for the two-dimensional DOA estimation method based on MUSIC and OMP proposed in the present application is shown in fig. 7, where the computer device may include: at least one memory 71, at least one processor 72, and at least one communication module 73, wherein:

the memory 71 may be used to store a program for implementing the two-dimensional DOA estimation method based on MUSIC and OMP described in the above method embodiments; processor 72 may load and execute the memory-stored program to perform the steps of the two-dimensional DOA estimation method based on MUSIC and OMP described above in relation to the method embodiments. The present application is not limited as to the type of devices of the memory 71 and the processor 72, as the case may be.

The communication module 73 may include, but is not limited to, a communication module for implementing data interaction by using a wireless communication network, such as a WIFI module, a 5G/6G (fifth generation mobile communication network/sixth generation mobile communication network) module, a GPRS module, an antenna, and the like, so as to implement data communication between the computer device and other devices.

It should be understood that the structure of the computer device described in the above embodiments does not constitute a limitation to the computer device in the embodiments of the present application, and in practical applications, the computer device may include more components than those described above, or may combine some components, such as a power supply, various sensor modules, and the like, which are not listed herein.

Finally, it should be noted that, in the embodiments, relational terms such as first, second and the like may be used solely to distinguish one operation, unit or module from another operation, unit or module without necessarily requiring or implying any actual such relationship or order between such units, operations or modules. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, or system that comprises the element.

The embodiments in the present description are described in a progressive or parallel manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device and the computer equipment disclosed by the embodiment correspond to the method disclosed by the embodiment, so that the description is relatively simple, and the relevant points can be referred to the method part for description.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (10)

1. A two-dimensional DOA estimation method based on MUSIC and OMP is characterized by comprising the following steps:

determining single-snapshot echo data received by each array element in the rectangular array;

performing one-dimensional multi-signal classification MUSIC operation on the echo data received by the array elements in the same direction to obtain a middle angle set and a pitch angle set of different targets; the snapshot data used by the one-dimensional MUSIC operation is determined according to the structure of the array element in the other direction, and the middle angle of any target is associated with the pitch angle and the azimuth angle of the target;

according to an orthogonal matching pursuit OMP algorithm, matching processing is carried out on a plurality of intermediate angles in the intermediate angle set and a plurality of pitching angles in the pitching angle set, and target intermediate angles and target pitching angles corresponding to different targets are determined;

and obtaining a target azimuth corresponding to the target according to the target intermediate angle and the target elevation and depression angle.

2. The method of claim 1, wherein the performing a one-dimensional multiple signal classification MUSIC operation on the echo data received from array elements in the same direction to obtain a middle angle set and a pitch angle set of different targets comprises:

arranging the echo data received by each array element according to the spatial sequence of the rectangular array to obtain an array echo matrix;

according to the array echo matrix and the first dimension of the array element in the pitching direction, a first data covariance matrix aiming at the array element in the azimuth direction is obtained;

obtaining a second data covariance matrix aiming at the pitching direction array element according to the array echo matrix and the second dimension of the azimuth direction array element;

obtaining a first steering vector matrix of each array element in different directions;

and carrying out power spectrum peak value search according to the first data covariance matrix, the second data covariance matrix and the first guide vector matrix in the corresponding direction to obtain a middle angle set and a pitching angle set corresponding to each target in different directions.

3. The method of claim 2, wherein performing a power spectrum peak search based on the first and second data covariance matrices and the first steering vector in the corresponding direction to obtain a set of intermediate angles and a set of pitch angles for each target in different directions comprises:

respectively performing characteristic decomposition on the first data covariance matrix and the second data covariance matrix, and obtaining array noise characteristic vectors corresponding to different directions according to the number of the targets;

carrying out peak value search according to the array noise characteristic vector and the first guide vector array in the same direction to obtain maximum values with the same number as the targets in the direction;

and forming a middle angle set and a pitching angle set of different targets by using the angles corresponding to the maximum values in the same direction.

4. The method of claim 2, wherein the determining the target intermediate angles and the target pitch angles corresponding to the different targets by matching a plurality of intermediate angles in the set of intermediate angles and a plurality of pitch angles in the set of pitch angles according to an Orthogonal Matching Pursuit (OMP) algorithm comprises:

vectorizing the two-dimensional echo data received by the rectangular array to obtain corresponding one-dimensional echo data;

obtaining a second steering vector matrix corresponding to the one-dimensional echo data according to the first steering vector matrix of each array element in different directions;

performing sparse vector reconstruction by utilizing an Orthogonal Matching Pursuit (OMP) algorithm according to the second steering vector matrix, the intermediate angle set and the pitching angle set;

and matching each two-dimensional estimation angle in the two-dimensional estimation angle set according to the position of a nonzero element in the reconstructed sparse vector to obtain a target middle angle and a target elevation angle corresponding to different targets.

5. The method of claim 4, wherein said performing sparse vector reconstruction using an Orthogonal Matching Pursuit (OMP) algorithm based on said second steering vector matrix, said set of intermediate angles, and said set of pitch angles comprises:

combining a plurality of intermediate angles in the intermediate angle set with a plurality of elevation angles in the elevation angle set to obtain two-dimensional estimation angle sets of different targets;

according to the second steering vector matrix, a steering matrix corresponding to the two-dimensional estimation angle set is obtained;

constructing a compressed sensing model according to the guide array and the sparse vector;

and reconstructing sparse vectors in the compressed sensing model by utilizing an Orthogonal Matching Pursuit (OMP) algorithm.

6. The method of claim 4, further comprising:

and determining the signal amplitude corresponding to the target according to the numerical value of the nonzero element in the reconstructed sparse vector.

7. The method of any one of claims 1-6, wherein determining the single-beat echo data received by each array element in the rectangular array comprises:

obtaining relative coordinates of each array element in the rectangular array in different directions and signal wavelengths of each target incident by the rectangular array;

modeling is carried out at least according to the relative coordinates and the signal wavelength to obtain an echo signal model;

and obtaining single-snapshot echo data received by each array element in the rectangular array by using the echo signal model.

8. An apparatus for two-dimensional DOA estimation based on MUSIC and OMP, the apparatus comprising:

the echo data determining module is used for determining the single-snapshot echo data received by each array element in the rectangular array;

the one-dimensional MUSIC operation module is used for carrying out one-dimensional multi-signal classification MUSIC operation on the echo data received by the array elements in the same direction to obtain a middle angle set and a pitching angle set of different targets; the snapshot data used by the one-dimensional MUSIC operation is determined according to the structure of the array element in the other direction, and the middle angle of any target is associated with the pitch angle and the azimuth angle of the target;

the matching processing module is used for matching a plurality of intermediate angles in the intermediate angle set and a plurality of pitching angles in the pitching angle set according to an orthogonal matching pursuit OMP algorithm, and determining target intermediate angles and target pitching angles corresponding to different targets;

and the target azimuth angle obtaining module is used for obtaining a target azimuth angle corresponding to the target according to the target intermediate angle and the target pitch-and-pitch angle.

9. The apparatus of claim 8, wherein the one-dimensional MUSIC operation module comprises:

the array echo matrix obtaining unit is used for arranging the echo data received by each array element according to the spatial sequence of the rectangular array to obtain array echo matrixes with different structures;

the first data covariance matrix obtaining unit is used for obtaining a first data covariance matrix aiming at the azimuth direction array element according to the array echo matrix corresponding to the azimuth direction and the first dimension of the elevation direction array element;

a second data covariance matrix obtaining unit, configured to obtain a second data covariance matrix for the elevation direction array element according to the array echo matrix corresponding to the elevation direction and a second dimension of the azimuth direction array element;

the first steering vector matrix obtaining unit is used for obtaining a first steering vector matrix of each array element in different directions;

and the peak searching unit is used for performing power spectrum peak searching according to the first data covariance matrix, the second data covariance matrix and the first guide vector matrix in the corresponding direction to obtain a middle angle set and a pitching angle set corresponding to each target in different directions.

10. A computer-readable storage medium, having stored thereon a computer program which, when loaded for execution by a processor, implements the MUSIC and OMP based two-dimensional DOA estimation method according to any of claims 1 to 7.

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